Optimal Multi-Objective Energy Management of Renewable Distributed Integration in Smart Distribution Grids Considering Uncertainties

Abstract

The energy supplies problem keeps floating on the surface. For this, daily improvements are implemented to do the optimization of the power system configuration and the generators’ power. Renewable Distributed Generators (RDGs) represent one of the best solutions, also a reference for those improvements. The optimal placement and sizing of RDG sources in the Smart Distribution Grid (SDG) are considered a trendy problem that usually can be solved based on the utilization of various approaches and algorithms due to their high complexity. 84The presence of RDGs in the SDG can provide many benefits and advantages. These benefits may be summarized generally as, power losses minimization, voltage profiles improvement, system load-ability and reliability growth, system security, and protection enhancement. To achieve the mentioned benefits, RDGs should be optimized in location and size based on various objective functions. A recent nature-inspired metaheuristic approach called the Marine Predators Algorithm (MPA) is used, which is based on various foraging strategies among optimal encounter rates policy in biological interaction and ocean predators. This algorithm was utilized to optimize many types of RDG units to obtain an optimal location and sizing of Photovoltaic Distributed Generator (PVDG) and Wind Turbine Distributed Generator (WTDG) units into the SDG considering uncertainties. This was performed when taking into consideration the uncertainties of the power generated from the RDG as well as the load demand variation during each of the day’s hours. This chapter proposed a new Multi Objective Indices (MOI) which is considered to minimize simultaneously five technical indices based on the power losses, the voltage deviation, and the overcurrent protection system of the SDG. The chosen algorithm is validated on different standards IEEE 33-bus, and 69-bus distribution grids for the purpose of testing its efficiency, where also three cases of RDGs’ allocation were studied. The convergence characteristics reveal that the MPA was effectively a quick technique that may arrive at the best solutions in a small iterations’ number compared to the other algorithms: Particle Swarm Optimization (PSO), Ant Lion Optimization (ALO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), and Moth Flame Optimizer (MFO) algorithms. The optimal allocation of RDG identifies the suitable results in the satisfaction of the permissible voltage limits and power loss minimization. After the installation of both RDGs, the power losses are minimized, the profile of the voltage has more augmented, and the overcurrent protection system had a considerable improvement. The simulation results confirm the feasibility of optimal power planning. In Addition, the obtained results reveal that the optimal integration of the WTDG units based on the chosen algorithm was the best choice over the PVDG units, which led to the minimization of the expected APL, RPL, VD, OT, and CTI of different test systems.